In the simplest of terms, the term ‘Big Data’ is used to describe an enormous volume of data, irrespective of its form (structured or unstructured). This data is overwhelmingly flooded by organizations and can be analyzed to get a better insight into the process and make better decisions in the future.
The inclusion of AI (Artificial Intelligence) has made a huge difference in Big Data. Before the advent of AI, entering data on a database or moving it from one destination to another was manual, mechanical, and time-consuming. Moreover, there were many strata of risks for error involved.
However, AI has managed to perform these tasks faster and more efficiently. This has made it easier for data scientists to acquire data for their research and strategies in a much better way.
Big Data is a very vital part of massive corporations and MNCs. The data volume is a valuable asset, but what companies do with this collected data carries much more value. More the efficiency in utilization, the higher the potential to grow.
Here are some elements that the optimum collection and utilization of Big Data can help in achieving:
- Smart business decision making is the foremost importance of Big Data as it can help identify the most efficient strategy for doing business.
- Real-time analysis is also a hailed advantage of Big Data as such a huge amount of data can help make complex decisions and take real-time actions.
- Understanding the consumer sentiments can be the trump card that drives growth, thanks to the analytics made from Big Data.
- Fraud detection and risk management in banking transactions are also possible with the proper utilization of Big Data.
- Anti-money laundering one of the high values and recent phenomenons that is driven by the analysis made by Big Data.
Apart from these elements, there are many other advantages that the data can impart to big corporate entities. Managing huge chunks of data can prove overwhelming for any human and is too difficult to process just via human resources optimally. Hence, it is imperative to process Big Data with AI. It eases out the effort put in the mechanical data entry processes and ensures a streamlined flow, movement, storing, and sharing of data for the concerned parties.
Interdependencies of Big Data and Artificial Intelligence
Big Data and Artificial Intelligence have imbibed in our daily lives much more than one can imagine. Just imagining what giant corporations like Amazon and Google do daily with the massive amount of data they generate can be overwhelming. How do they process billions of data entries daily? And most importantly, how does this data help them improve their services?
Now, to put things in perspective, every user is unique in Google and Amazon (the examples we have taken here). So, personalized and relevant recommendations are set based on the user’s behavior and search pattern. The “Did you mean” section on Google search results and the “product recommendations” on Amazon work on the amalgamation of Big Data in AI.
Even Facebook has a profound integration of AI in processing the terabytes of data collected from its users. All the posts, shares, photos, videos, comments, tags, etc., are a part of the collected Big Data. This data is then used to make the user experience better. Please think of how easy it is to tag people on Facebook. You select the tagging option, and you get recommendations based on your behavior on the platform, like previous tags, comments, likes, messages, etc.
That’s how prompt Big Data can get once it is integrated with the wonders of AI.
How Big Data & Artificial Intelligence feed on each other
Big Data, as collected at the source, is the great raw material for growth analysis and strategy development. However, this massive and overwhelming amount of data could do very little, or take a lot of time to decode, if there was no AI.
In other words, AI is the fuel that drives large corporate entities to make the most use of Big Data. AI and Big Data are interdependent and feed off each other.
But what role does Big Data play in channelizing AI in the right direction? Let’s find out.
Big Data helps us experimenting with AI.
Machine learning is what AI works on. However, implementing such an expensive setup is quite difficult if there is no data to work on. Big Data is inevitably huge, and AI needs something this massive to work most efficiently.
Moreover, AI and machine learning do not, by default, have emotional intelligence yet. However, this feature can be imbibed in AI via experiments on Big Data. It is much like AB Testing, a trial-and-error method.
Big Data helps in diversifying AI.
As already pointed out, implementing AI is a costly affair. However, with the use of Big Data, implementing such machine learning technology is slowly getting more cost-effective. This will enable a greater number of organizations all around the world to implement AI in analyzing and implementing strategies developed from Big Data.
So, Big Data is a powerful force behind diversifying AI globally.
Customer Behaviour and Consumer Insights
Market analysis is one of the core significance of the implementation of Big Data in AI. With the lack of versatile data, it would be impossible to determine consumer behavior and develop strategies. Short-term or insufficient data will do little to boost growth.
However, the collection of Big Data is the food that feeds AI and offers a large amount of data repositories about consumer behavior. It thereby enables machine learning to make the most use of it.
Search Engines and Cognitive Platforms
Big Data answers a lot of questions too. AI requires a lot of data to have a crack on data scientists and define the strategies. This means machine learning requires a lot of questions to be answered, like:
- What is the amount of data available?
- Is the data biased or not?
- What are the Ethical considerations, if any?
These questions are the labels that are worked on by AI with the help of the data available. So, if these data points weren’t available, AI implementation wouldn’t be as smooth and accurate as it is today.
Evolution of Data Engineering in Big Data
Advanced data analytics for Big Data has faced many challenges, but as highlighted previously, optimum data management at a huge scale was the most prominent challenge. Big Data engineering, thus, had to look further than traditional technologies like Hadoop, Yarn, HDFS, etc., to ensure that Big Data engineering reached new heights.
Adopting technologies like Cloud, Spark, Kafka, and serverless have all elevated Big Data Engineering and elevated the impact of AI on data management. This became possible due to these technologies’ extraordinarily faster processing of data, enabled through effective uncoupling of storage and computation.
Data scientists, analysts, and business users are all dependent on Big Data, and it is accessible to them through high-end data engineering. The privation or lack of such professionals who are adept in data engineering has been the main reason why Big Data management’s scalability didn’t take off as it should have. But now, businesses are promoting the modernization of their analytics environment and ensuring proper resource allotment to data engineering.
Why do you need Big Data skilling for data engineering
Big data has been a revolutionary step towards the implementation of AI in the digital world. It depends largely on data engineers to perform various tasks, like data ingestion, transformation, and performance optimization, to ensure the most user-friendly and lucrative business structure.
However, this is not the easiest thing to do. The tasks that big data engineers need to involve include:
- The building, designing, and maintaining a pipeline
- Aggregate, accumulate, and transform the data that are acquired from various sources.
- Develop an analytics process, optimize the results based to ensure the better business outcome
Keeping these in mind, there are a few essential skills that big data engineers should learn to ensure optimum utilization of the data acquired. The market is ever-changing and needs to adopt and implement innovations to garner the best results. These skills are:
- NoSQL, replacing the traditional SQL
NoSQL databases include MongoDB and Couchbase and are fast-replacing the traditional databases, like Oracle, etc. Massive corporations should provide expertise in this technology to complement the knowledge in Apache Hadoop. The two combined might help loads in data crunching.
- Machine learning
As far as machine learning goes, it is one of the hot trends in almost every sector: retail, e-commerce, insurance, and IT. The professional data scientists, who are into data mining to analyze and predict, should be experts in using big data for personalization, recommendation, and classification.
- Apache Spark
A lot of businesses use MapReduce, which is a complex technology in big data and artificial intelligence. However, Apache Spark is a more straightforward and simple alternative to MapReduce. Plus, the fact that it has a better in-memory stack makes it the ideal technology to gain expertise on.
- Data mining
Now, data mining is not the exact skill that the IT sectors should emphasize on. However, what matters is the tools that are used for data mining. Some of the most sought-after tools in the field are Apache Mahout, KNIME, and Rapid Miner. These tools have their own set of advantages and are easier to implement for data miners and scientists.
These are some key points to understand the significance of Big Data in AI and vice versa. There have been thousands of MNCs worldwide that have successfully implemented AI to make the most use of Big Data to stay competitive and improve business performance.